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20121201
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on earth. best way to play it, kansas city southern. the new railroad running north/south across north america, or as they sometimes call themselves, the nafta railroad. at last, maybe we can get some revenge for all of the jobs nafta has caused us. all of the losses. anyway, ksu is my new favorite rail. and i think you can buy it at the weakest because it's got the tracks where people want them and little competition to boot. let's go to greg in mississippi, greg? >> yes, mr. cramer, thank you so much for taking my call. >> my pleasure. >> caller: i had the pleasure of speaking to you on the "lightning round" a couple of months ago about nordic tanker. >> yeah, go ahead, i'm sorry. >> caller: no, no, i'm sorry, at that time you recommended me not go into that particular stock. i was just wondering, you highly recommended it in your book getting back to even which i thoroughly enjoyed, and i was curious if anything changed in particular with the company or that sector in general. >> i had to back away from it. and i think those who know the show and watch it know that i've backed away
% in the south and in the northeast. so we had great strength in many areas but areas like the government, the center region, canada, latin america, we had poor execution. >> now, you do some work -- this is the first time i asked you about this. for the oil and gas industry. what do you guys do for oil and gas? we know what do you for amazon, what you do for retailers and for government. i never heard you talk about this sector. >> oil and gas is a big data problem. we look at massive amounts of data and define patterns so people know where to drill so that's on one hand and then on the other hand it's an integration problem. they have lots of sources of data that need to be integrated and they need to manage their supply chains so we are the infrastructure for that industry. >> so in other words like they get a reservoir map and you figure out what is likelihood of where oil could be found. >> yeah, yeah. we look at massive amounts of data and we find patterns in that data. no different than what i do with my basketball team. who do i sell jerseys do and who do i sell tickets to? where
Search Results 0 to 2 of about 3 (some duplicates have been removed)

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